Methods, systems, and devices for predicting a state of a user (e.g., asleep or awake). In some embodiments, the location sharing system accesses historical activity data of the user and extracts historical sleep records from the historical activity data. The system clusters the historical sleep records into a plurality of clusters and extracts a sleep pattern from each one of the plurality of clusters. Then, when the location sharing system receives current activity data of the user, the system can predict whether the user is currently asleep based on the current activity of the user and at least one of the sleep patterns. Some embodiments additionally compute an estimated wake up time of the user. Some embodiments share the predicted physiological state of the user with the user's friends via the map GUI. Some embodiments additionally share the estimated wake up time of the user.
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1. A method comprising: receiving, from a first client device associated with a first user of a messaging system, a first indication of current activity data of the first user; determining that the first user is currently asleep based on the current activity data of the first user and on a plurality of sleep patterns for the first user, the plurality of sleep patterns being based on historical sleep records determined from historical activity data of the first user; and sending, to a second client device associated with a second user of the messaging system and based on the determining, a second indication that the first user is currently asleep.
This invention relates to a messaging system that detects and communicates a user's sleep state to another user. The system receives current activity data from a first user's client device, such as movement, screen interaction, or other sensor inputs, to determine whether the user is asleep. The determination is based on both real-time activity data and historical sleep patterns derived from past activity records. If the system identifies that the first user is asleep, it sends a notification to a second user's device, informing them of the first user's sleep state. This allows the second user to avoid sending messages that may disturb the sleeping user. The system may also use machine learning or statistical analysis to refine sleep pattern detection over time. The invention improves user experience by reducing unnecessary notifications and respecting the sleep schedules of messaging system users.
2. The method of claim 1 , wherein the second indication is sent as an ephemeral message accessible by the second client device for a predetermined duration of time.
This invention relates to secure communication systems, specifically methods for transmitting ephemeral messages between client devices. The problem addressed is the need for temporary, time-limited message access in digital communication to enhance privacy and security. The invention provides a method where a first client device sends a message to a second client device, with the message being accessible only for a predetermined duration before automatic deletion. This ensures that sensitive information is not permanently stored or accessible beyond the intended timeframe. The system involves generating a first indication of the message's availability, followed by a second indication that the message is ephemeral, meaning it will expire after a set period. The second indication includes metadata specifying the duration for which the message remains accessible. The second client device receives and displays the message only during this predefined time window, after which the message is automatically deleted or becomes inaccessible. This approach prevents unauthorized access or retention of the message beyond the intended duration, improving data security in communication systems. The method can be applied in messaging applications, enterprise communication platforms, or any system requiring temporary data sharing.
3. The method of claim 2 , further comprising: receiving, from the second client device, a request to access the ephemeral message; causing, in response to receiving the request, the ephemeral message to be displayed on the second client device; and ceasing display of, and access to, the ephemeral message upon expiration of the predetermined duration of time.
This invention relates to a system for managing ephemeral messages, which are messages that automatically disappear after a predetermined duration. The system involves a server that receives an ephemeral message from a first client device and stores it for a specified time. The server then receives a request from a second client device to access the message and, in response, displays it on the second device. Once the predetermined duration expires, the server ceases display and access to the message, ensuring it is no longer available. The system may also include a notification mechanism to inform the second client device when the message is about to expire. The invention addresses the need for secure, time-limited communication by automatically removing messages after a set period, enhancing privacy and reducing the risk of unauthorized access. The method ensures that ephemeral messages are only accessible for the intended duration, after which they are permanently deleted or otherwise made inaccessible. This approach is particularly useful in applications where temporary, self-destructing messages are required, such as in secure communication platforms or social media.
4. The method of claim 1 , wherein determining that the first user is currently asleep comprises: determining that the current activity data of the first user verifies a set of criteria; selecting one of the plurality of sleep patterns based on the current activity data of the first user; and computing a probability of the first user being currently asleep based on the selected sleep pattern, wherein the first user is determined to be currently asleep based on the computed probability exceeding a threshold.
This invention relates to systems and methods for detecting whether a user is currently asleep by analyzing activity data and comparing it to predefined sleep patterns. The problem addressed is the need for accurate and reliable sleep detection to enable context-aware applications, such as adjusting device settings, scheduling notifications, or triggering health monitoring. The method involves collecting current activity data of a user, such as movement, heart rate, or environmental sensors. The system verifies whether this data meets a set of predefined criteria indicative of sleep, such as prolonged inactivity or low heart rate variability. If the criteria are met, the system selects a sleep pattern from a plurality of stored patterns based on the user's current activity data. These patterns may represent different sleep stages or individual sleep behaviors. The system then computes a probability that the user is asleep by comparing the current activity data to the selected sleep pattern. If this probability exceeds a predefined threshold, the user is determined to be currently asleep. This determination can be used to trigger actions like silencing notifications or initiating sleep tracking. The approach improves accuracy by dynamically selecting the most relevant sleep pattern and applying probabilistic assessment rather than binary thresholds.
5. The method of claim 4 , further comprising computing a predicted wake up time based on the selected sleep pattern.
This invention relates to systems for optimizing sleep patterns to improve wakefulness and performance. The problem addressed is the lack of personalized sleep recommendations that account for individual sleep needs and external factors to ensure optimal wake times. The method involves analyzing sleep data, such as sleep stages and duration, to determine a user's sleep efficiency. Based on this analysis, a sleep pattern is selected from a predefined set of patterns, each associated with different sleep durations and structures. The selected pattern is then adjusted based on external factors like scheduled activities or environmental conditions to generate a customized sleep schedule. Additionally, the method computes a predicted wake-up time by evaluating the selected sleep pattern, ensuring the user wakes up at an optimal time aligned with their natural sleep cycles. This approach aims to enhance alertness and productivity by aligning sleep with biological rhythms and personal constraints. The system may also include feedback mechanisms to refine recommendations over time.
6. The method of claim 4 , wherein the set of criteria includes that the first client device is static.
A method for managing network resources in a wireless communication system addresses the challenge of efficiently allocating bandwidth and reducing interference in environments with multiple mobile and static devices. The method involves monitoring the movement of client devices within a network coverage area to determine their mobility status. A static device is identified as one that remains in a fixed position for a prolonged period, while a mobile device is one that moves frequently. The method then applies different resource allocation strategies based on the mobility status of each device. For static devices, the system may prioritize stable, high-quality connections by assigning dedicated channels or reducing handover frequency. For mobile devices, the system may optimize for seamless handover and dynamic bandwidth allocation to accommodate movement. The method may also include adjusting transmission power or beamforming parameters based on the mobility status to improve overall network efficiency and reduce interference. By distinguishing between static and mobile devices, the system can enhance network performance, reduce resource waste, and improve user experience in both stationary and dynamic environments.
7. The method of claim 4 , wherein the set of criteria includes that no user input has been detected at the first client device for a preset period of time.
A system and method for detecting inactivity at a client device to trigger an automated action. The technology addresses the problem of determining when a user has stopped interacting with a device, such as a computer or mobile device, to conserve resources, enhance security, or initiate predefined actions. The method involves monitoring user input at a first client device and applying a set of criteria to determine inactivity. One key criterion is the absence of user input for a preset period of time, which may include keyboard strokes, mouse movements, touchscreen interactions, or other input signals. When the criteria are met, the system may trigger actions such as locking the device, suspending processes, or initiating a power-saving mode. The method may also involve additional criteria, such as analyzing device usage patterns or network activity, to refine inactivity detection. The system ensures accurate detection of user absence while minimizing false positives, improving efficiency and security. The solution is applicable in various computing environments, including personal computers, mobile devices, and embedded systems, where automated responses to inactivity are beneficial.
8. The method of claim 4 , wherein the set of criteria includes that a location of the first user is not within a preset distance of the location of a second user.
This invention relates to user location-based systems, specifically methods for managing user interactions or access based on proximity criteria. The problem addressed is ensuring that certain actions or interactions are only permitted when users are physically separated by a predefined distance, preventing unwanted or unauthorized interactions when users are too close. The method involves evaluating a set of criteria to determine whether an action should be allowed. One key criterion is that the location of a first user must not be within a preset distance of a second user's location. This ensures that the action is only permitted when the users are sufficiently far apart. The system may use GPS, Wi-Fi, or other location-tracking technologies to determine user positions. The preset distance can be fixed or dynamically adjusted based on factors like user preferences, environmental conditions, or system policies. Additional criteria may include time-based restrictions, user authentication, or permission levels. The method can be applied in various scenarios, such as social media platforms, security systems, or access control mechanisms, where proximity-based restrictions are necessary. The system may also provide alerts or notifications if the distance criterion is violated, allowing users to take corrective action. The goal is to enhance privacy, security, or operational efficiency by enforcing spatial separation rules.
9. The method of claim 4 , wherein the set of criteria includes that a location of the first user is within a preset distance of the location of a domicile of the first user.
This invention relates to location-based systems for determining user eligibility for services or features, particularly in the context of user authentication or access control. The problem addressed is ensuring that a user is physically present at or near their registered domicile before granting access to certain services, enhancing security and preventing unauthorized use from remote locations. The method involves evaluating a set of criteria to determine whether a first user meets specific conditions for accessing a service or feature. One key criterion is that the user's current location must be within a preset distance of their registered domicile. This ensures that the user is physically near their home or a designated location, reducing the risk of fraudulent access from distant or unauthorized locations. The system may use GPS, Wi-Fi, or other location-tracking technologies to verify the user's position in real time. Additional criteria may include time-based restrictions, device authentication, or behavioral patterns to further validate the user's identity and intent. By combining location proximity with other verification factors, the method provides a robust mechanism for securing access to sensitive services, such as financial transactions, account management, or location-specific features. The approach is particularly useful for preventing unauthorized access when a user's device or credentials are compromised, as it enforces a physical presence requirement. The preset distance can be adjusted based on security needs, balancing convenience and protection.
10. The method of claim 4 , wherein each historical sleep record comprises an historical timetable comprising a plurality of historical time slots, each historical time slot being associated with a probability of the first user being asleep during the historical time slot, the probability of the first user being asleep during the historical time slot being computed based on the historical activity data verifying the set of criteria for the historical time slot.
This invention relates to sleep monitoring systems that analyze historical activity data to predict sleep patterns. The problem addressed is the need for accurate sleep tracking by leveraging past activity data to determine when a user is likely asleep. The method involves generating historical sleep records, each containing a timetable with multiple time slots. Each time slot is assigned a probability indicating the likelihood that the user was asleep during that period. This probability is calculated by evaluating historical activity data against predefined criteria for the specific time slot. The criteria may include factors such as movement, device usage, or other indicators of wakefulness. By analyzing these patterns over time, the system improves the accuracy of sleep predictions. The historical sleep records are used to refine future sleep predictions, ensuring more reliable tracking of sleep habits. This approach enhances sleep monitoring by incorporating probabilistic assessments based on verified historical data, reducing reliance on real-time sensors alone. The system is particularly useful for applications requiring long-term sleep analysis, such as health monitoring or personalized sleep recommendations.
11. The method of claim 1 , further comprising: accessing, from a database and prior to the receiving, the historical activity data of the first user; and determining the historical sleep records from the historical activity data.
The invention relates to a method for analyzing and utilizing user activity data, particularly focusing on sleep patterns. The method addresses the challenge of accurately tracking and interpreting sleep-related information from historical user activity data to improve health monitoring or personalized recommendations. The method involves accessing historical activity data of a user from a database before receiving any new data. From this historical data, the method extracts and determines historical sleep records, which may include metrics such as sleep duration, sleep quality, or sleep stages. These records are then used to analyze trends, identify patterns, or generate insights related to the user's sleep habits. The method may also involve comparing the historical sleep records with current or future activity data to assess changes or deviations in sleep behavior. By leveraging historical sleep records, the method enables more accurate and personalized sleep analysis, which can be applied in health monitoring systems, wearable devices, or sleep improvement applications. The approach ensures that sleep data is contextualized within a user's broader activity history, enhancing the reliability and relevance of the insights derived.
12. The method of claim 1 , further comprising: clustering the historical sleep records into a plurality of clusters; and extract a sleep pattern for each of the plurality of clusters so as to determine the plurality of sleep patterns.
This invention relates to sleep pattern analysis using historical sleep records. The problem addressed is the need to identify distinct sleep patterns from large datasets of sleep data to improve personalized sleep monitoring and recommendations. The method involves processing historical sleep records, which may include data such as sleep duration, sleep stages, and sleep quality metrics. The records are clustered into multiple groups based on similarities in sleep characteristics. Each cluster represents a distinct sleep pattern, allowing for the identification of recurring sleep behaviors. By analyzing these patterns, the system can detect deviations from typical sleep behavior, which may indicate health issues or lifestyle changes. The extracted sleep patterns can be used to provide personalized sleep recommendations, adjust sleep tracking parameters, or alert users to potential sleep disturbances. The clustering process may use algorithms such as k-means, hierarchical clustering, or other machine learning techniques to group similar sleep records. The extracted patterns can be further refined by incorporating additional data, such as environmental factors or user activity, to enhance accuracy. This approach enables more precise sleep monitoring and tailored interventions for individuals with varying sleep habits.
13. The method of claim 12 , wherein each one of the sleep patterns comprises a generic timetable comprising a plurality of generic time slots, each generic time slot being associated with a probability of the first user being asleep during the generic time slot, and wherein, for each generic time slot, extracting a sleep pattern from one of the plurality of clusters comprises: retrieving, from the historical sleep records included in the cluster, one or more probabilities of the first user being asleep during one or more historical time slots matching the generic time slot, and computing a probability of the first user being asleep during the generic time slot, the probability of the first user being asleep during the generic time slot being computed based on the one or more probabilities of the first user being asleep during the one or more historical time slots matching the generic time slot.
This invention relates to sleep pattern analysis and prediction, specifically improving the accuracy of sleep pattern extraction from historical sleep records. The problem addressed is the variability in individual sleep patterns, which makes it difficult to predict sleep behavior reliably. The solution involves clustering historical sleep records into groups (clusters) based on similarity, where each cluster represents a distinct sleep pattern. Each sleep pattern is defined by a generic timetable composed of multiple time slots, each associated with a probability that the user is asleep during that time slot. To extract a sleep pattern from a cluster, the method retrieves probabilities of the user being asleep during historical time slots that match the generic time slot. The probability for the generic time slot is then computed based on these historical probabilities, allowing for more accurate sleep pattern predictions. This approach leverages historical data to refine sleep pattern models, improving the reliability of sleep predictions for individuals. The method is particularly useful in applications requiring personalized sleep monitoring, such as health tracking or smart home automation.
14. The method of claim 1 , wherein each historical sleep record is associated with one of a plurality of categories, and wherein clustering the historical sleep records into a plurality of clusters comprises grouping together the historical sleep records associated with one of the categories.
15. The method of claim 14 , wherein the categories are days of a week.
A system and method for organizing and retrieving data based on temporal categories, particularly days of the week, to improve accessibility and usability. The invention addresses the challenge of efficiently categorizing and retrieving information in digital systems, where users often struggle to locate relevant data due to unstructured or overly broad classification schemes. The method involves assigning data entries to specific days of the week, allowing users to filter or search for information based on temporal relevance. This categorization can be applied to various types of data, including but not limited to schedules, reminders, or task lists. The system may also include a user interface that displays data entries grouped by day, enabling quick visualization and retrieval. Additionally, the method may support dynamic updates, where data entries can be reassigned to different days as needed. The invention enhances productivity by reducing search time and improving organization, particularly in applications where time-based categorization is critical, such as calendar systems, project management tools, or personal productivity apps. The method ensures that users can efficiently access information based on the day it is relevant, improving workflow efficiency and user experience.
16. A system comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the processor to perform operations comprising: receiving, from a first client device associated with a first user of a messaging system, a first indication of current activity data of the first user; determining that the first user is currently asleep based on the current activity data of the first user and on a plurality of sleep patterns for the first user, the plurality of sleep patterns being based on historical sleep records determined from historical activity data of the first user; and sending, to a second client device associated with a second user of the messaging system and based on the determining, a second indication that the first user is currently asleep.
This system monitors user activity to detect sleep patterns and notify contacts when a user is asleep. The system operates within a messaging platform, where a processor and memory execute instructions to analyze activity data from a first user's device. The system receives real-time activity data, such as device usage or movement, and compares it against historical sleep records to determine if the user is currently asleep. Historical activity data is used to establish personalized sleep patterns, improving accuracy in detecting sleep states. When the system identifies that the first user is asleep, it sends a notification to a second user's device, informing them of the first user's sleep status. This allows contacts to avoid sending messages that may disturb the sleeping user. The system leverages machine learning or pattern recognition to refine sleep detection over time, adapting to changes in the user's sleep habits. The solution addresses the problem of unwanted notifications disrupting sleep by providing automated, context-aware communication status updates.
17. The system of claim 16 , wherein the second indication is sent as an ephemeral message accessible by the second client device for a predetermined duration of time.
This invention relates to a system for secure communication between client devices, addressing the need for temporary, time-limited message sharing. The system includes a first client device that generates a first indication of a shared resource, such as a file or data, and a second client device that receives a second indication of the shared resource. The second indication is sent as an ephemeral message, meaning it is accessible only for a predetermined duration before being automatically deleted. This ensures that sensitive or time-sensitive information is not permanently stored or accessible beyond the intended timeframe. The system may also include a server that facilitates the exchange of these indications between devices, ensuring secure and controlled access to the shared resource. The ephemeral nature of the second indication enhances privacy and security by preventing unauthorized or prolonged access to the shared data. The system may further include mechanisms for verifying the identity of the second client device before granting access to the shared resource, adding an additional layer of security. This approach is particularly useful in scenarios where temporary access to confidential or time-sensitive information is required, such as in business collaborations, healthcare, or legal communications.
18. The system of claim 17 , the operations further comprising: receiving, from the second client device, a request to access the ephemeral message; causing, in response to receiving the request, the ephemeral message to be displayed on the second client device; and ceasing display of, and access to, the ephemeral message upon expiration of the predetermined duration of time.
This invention relates to a system for managing ephemeral messages, which are messages that automatically disappear after a predetermined duration. The system addresses the problem of ensuring that ephemeral messages are securely and reliably displayed only for the intended time period, then automatically removed to prevent unauthorized access or retention. The system includes a server that stores ephemeral messages and manages their lifecycle, including their creation, distribution, display, and expiration. The server receives a request from a first client device to send an ephemeral message to a second client device, where the message is configured to be displayed for a specific duration before being automatically deleted. The server then transmits the ephemeral message to the second client device, which displays the message for the predetermined time. Upon expiration of that time, the system ceases display of the message and revokes access to it, ensuring it cannot be viewed or retrieved afterward. The system may also include additional features such as tracking message delivery status and notifying the sender when the message has been viewed or has expired. This ensures secure, time-limited communication while preventing unauthorized retention or distribution of the content.
19. The system of claim 16 , wherein determining that the first user is currently asleep comprises: determining that, the current activity data of the first user verifies a set of criteria; selecting one of the plurality of sleep patterns based on the current activity data of the first user; and computing a probability of the first user being currently asleep based on the selected sleep pattern, wherein the first user is determined to be currently asleep based on the computed probability exceeding a threshold.
A system monitors a user's activity data to determine whether they are currently asleep. The system collects activity data from sensors or devices associated with the user, such as motion sensors, biometric data, or environmental sensors. The system compares the current activity data against predefined criteria to assess whether the user is likely asleep. If the criteria are met, the system selects a sleep pattern from a plurality of stored sleep patterns based on the user's activity data. The system then computes a probability that the user is asleep by applying the selected sleep pattern to the activity data. If this probability exceeds a predefined threshold, the system concludes that the user is currently asleep. The system may use this determination to trigger actions, such as adjusting environmental conditions, notifying other users, or modifying device settings. The sleep patterns may be personalized or generalized, and the threshold may be adjustable to balance sensitivity and accuracy. The system may also update sleep patterns over time based on new activity data to improve future determinations.
20. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising: receiving, from a first client device associated with a first user of a messaging system, a first indication of current activity data of the first user; determining that the first user is currently asleep based on the current activity data of the first user and on a plurality of sleep patterns for the first user, the plurality of sleep patterns being based on historical sleep records determined from historical activity data of the first user; and sending, to a second client device associated with a second user of the messaging system and based on the determining, a second indication that the first user is currently asleep.
This invention relates to a messaging system that detects and communicates a user's sleep status to other users. The problem addressed is the lack of awareness among messaging system users about whether a contact is asleep, leading to unnecessary or disruptive messages. The system monitors a user's activity data, such as device interactions or sensor inputs, to determine if they are currently asleep. Historical sleep patterns derived from past activity data are used to refine this determination. When the system identifies that a user is asleep, it notifies another user's device, allowing them to avoid sending messages that may disturb the sleeping user. The system may also use machine learning or statistical models to analyze activity data and sleep patterns, improving accuracy over time. The invention ensures privacy by processing data locally or anonymously, preventing unauthorized access to sensitive sleep information. This approach enhances user experience by reducing disruptions and promoting respectful communication practices.
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June 30, 2020
March 15, 2022
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